import random
import json
import numpy as np

class Network(object):

    def __init__(self, sizes):
        self.num_layers = len(sizes)
        self.flag = True
        self.sizes = sizes
        # (30, 1) (10, 1)
        self.biases = [np.random.randn(y, 1) for y in sizes[1:]]
        # (30, 784) (10, 30)
        self.weights = [np.random.randn(y, x)
                        for x, y in zip(sizes[:-1], sizes[1:])]

    def feedforward(self, a):
        """Return the output of the network if ``a`` is input."""
        # 做了2次循环计算
        # (784,1) => (30,1) => (10,1)
        for b, w in zip(self.biases, self.weights):
            a = sigmoid(np.dot(w, a)+b)
        return a

    def SGD(self, training_data, epochs, mini_batch_size, eta,
            test_data=None):
        training_data = list(training_data)
        n = len(training_data)
        print('训练数据条数：',n)
        if test_data:
            test_data = list(test_data)
            n_test = len(test_data)
        print('测试数据条数：',n_test)
        for j in range(epochs):
            random.shuffle(training_data)
            mini_batches = [
                training_data[k:k+mini_batch_size]
                for k in range(0, n, mini_batch_size)]
            
            for mini_batch in mini_batches:
                self.update_mini_batch(mini_batch, eta)
            # 把训练的结果保存为txt文本
            # if j==4:
            #     f=open('a.txt',mode='a')
            #     f.write('weights=[')
            #     np.savetxt(f,self.weights[0],fmt='%0.3f',newline=',',delimiter=',')
            #     f.write(']\n')
            #     f.write('weights1=[')
            #     np.savetxt(f,self.weights[1],fmt='%0.3f',newline=',',delimiter=',')
            #     f.write(']\n')
            #     f.write('biases=[')
            #     np.savetxt(f,self.biases[0],fmt='%0.3f',newline=',')
            #     f.write(']\n')
            #     f.write('biases1=[')
            #     np.savetxt(f,self.biases[1],fmt='%0.3f',newline=',')
            #     f.write(']')
            #     f.close()
            if test_data:
                print("Epoch {} : {} / {}".format(j,self.evaluate(test_data),n_test));
            else:
                print("Epoch {} complete".format(j))

    def update_mini_batch(self, mini_batch, eta):
        nabla_b = [np.zeros(b.shape) for b in self.biases]
        nabla_w = [np.zeros(w.shape) for w in self.weights]
        # 循环进行了10次，10次梯度加起来除以10，得到平均数
        for x, y in mini_batch:
            delta_nabla_b, delta_nabla_w = self.backprop(x, y)
            nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)]
            nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]
        self.weights = [w-(eta/len(mini_batch))*nw
                        for w, nw in zip(self.weights, nabla_w)]
        self.biases = [b-(eta/len(mini_batch))*nb
                       for b, nb in zip(self.biases, nabla_b)]

    def backprop(self, x, y):
        nabla_b = [np.zeros(b.shape) for b in self.biases]
        nabla_w = [np.zeros(w.shape) for w in self.weights]
        # feedforward
        activation = x
        activations = [x] # list to store all the activations, layer by layer
        zs = [] # list to store all the z vectors, layer by layer
        # 循环2次
        for b, w in zip(self.biases, self.weights):
            z = np.dot(w, activation)+b
            zs.append(z)
            activation = sigmoid(z)
            activations.append(activation)
        # backward pass
        # activations[-1] (10,1) y (10,1)是答案
        # sigmoid_prime是sigmoid的导数
        delta = self.cost_derivative(activations[-1], y) * sigmoid_prime(zs[-1])
        nabla_b[-1] = delta
        # if(self.flag==True):
        #     print(zs[-1])
        #     print(self.cost_derivative(activations[-1], y))
        #     self.flag=False
        nabla_w[-1] = np.dot(delta, activations[-2].transpose())
        # 循环进行了一次，l=2
        for l in range(2, self.num_layers):
            z = zs[-l]
            sp = sigmoid_prime(z)
            delta = np.dot(self.weights[-l+1].transpose(), delta) * sp
            nabla_b[-l] = delta
            nabla_w[-l] = np.dot(delta, activations[-l-1].transpose())
        return (nabla_b, nabla_w)

    def evaluate(self, test_data):
        test_results = [(np.argmax(self.feedforward(x)), y)
                        for (x, y) in test_data]
        return sum(int(x == y) for (x, y) in test_results)

    def cost_derivative(self, output_activations, y):
        return (output_activations-y)

#### Miscellaneous functions
def sigmoid(z):
    """The sigmoid function."""
    return 1.0/(1.0+np.exp(-z))

def sigmoid_prime(z):
    """Derivative of the sigmoid function."""
    return sigmoid(z)*(1-sigmoid(z))
